Copertina

Knows: Agent-Native Structured Research Representations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yu, Guangsheng, Wang, Xu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning
di: Stoisser, Josefa Lia, et al.
Pubblicazione: (2025)

MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents
di: Nguyen, Thao, et al.
Pubblicazione: (2026)

PlanTwin: Privacy-Preserving Planning Abstractions for Cloud-Assisted LLM Agents
di: Yu, Guangsheng, et al.
Pubblicazione: (2026)

Agents Need Not Know Their Purpose
di: Garcia, Paulo
Pubblicazione: (2024)

LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
di: Fan, HuiMing, et al.
Pubblicazione: (2026)

Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
di: Liu, Wenhao, et al.
Pubblicazione: (2024)

K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control
di: Wu, Zhe, et al.
Pubblicazione: (2026)

KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation
di: Chen, Tongbo, et al.
Pubblicazione: (2026)

Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Process
di: FU, Zhizhang, et al.
Pubblicazione: (2025)

How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
di: Peng, Bo, et al.
Pubblicazione: (2026)

Native and Compact Structured Latents for 3D Generation
di: Xiang, Jianfeng, et al.
Pubblicazione: (2025)

Environment Maps: Structured Environmental Representations for Long-Horizon Agents
di: Feng, Yenchia, et al.
Pubblicazione: (2026)

CaRT: Teaching LLM Agents to Know When They Know Enough
di: Liu, Grace, et al.
Pubblicazione: (2025)

Do Agents Know What They Can't Do? Evaluating Feasibility Awareness in Tool-Using Agents
di: Cheng, Liang, et al.
Pubblicazione: (2026)

Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning
di: Zhao, Qiannian, et al.
Pubblicazione: (2026)

From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills
di: Liang, Qiliang, et al.
Pubblicazione: (2026)

SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
di: Jiang, Yanna, et al.
Pubblicazione: (2026)

From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems
di: Wang, Shaolin, et al.
Pubblicazione: (2026)

MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook
di: Feng, Yi, et al.
Pubblicazione: (2026)

HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?
di: Trinh, Tu, et al.
Pubblicazione: (2026)

What Do LLM Agents Know About Their World? Task2Quiz: A Paradigm for Studying Environment Understanding
di: Liu, Siyuan, et al.
Pubblicazione: (2026)

KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
di: Li, Rui, et al.
Pubblicazione: (2025)

CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
di: Liu, Junyuan, et al.
Pubblicazione: (2026)

Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation
di: Sacco, Gabriele, et al.
Pubblicazione: (2024)

Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs
di: Unlu, Eren
Pubblicazione: (2026)

Clawed and Dangerous: Can We Trust Open Agentic Systems?
di: Chen, Shiping, et al.
Pubblicazione: (2026)

KnowRL: Teaching Language Models to Know What They Know
di: Kale, Sahil, et al.
Pubblicazione: (2025)

Do LLM Agents Know How to Ground, Recover, and Assess? A Benchmark for Epistemic Competence in Information-Seeking Agents
di: Shao, Jiaqi, et al.
Pubblicazione: (2025)

ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
di: Zhao, Zhengyang, et al.
Pubblicazione: (2026)

CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
di: Chen, Zhengchao, et al.
Pubblicazione: (2025)

TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems
di: Zhang, Xiao, et al.
Pubblicazione: (2025)

UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge
di: Zhang, Yang, et al.
Pubblicazione: (2025)

The KnowWhereGraph Ontology
di: Shimizu, Cogan, et al.
Pubblicazione: (2024)

Open Agent Specification (Agent Spec): A Unified Representation for AI Agents
di: Amini, Soufiane, et al.
Pubblicazione: (2025)

Detecting RLVR Training Data via Structural Convergence of Reasoning
di: Zhang, Hongbo, et al.
Pubblicazione: (2026)

From Cloud-Native to Trust-Native: A Protocol for Verifiable Multi-Agent Systems
di: Li, Muyang
Pubblicazione: (2025)

CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation
di: Huang, Fuxian, et al.
Pubblicazione: (2024)

Do Retrieval Augmented Language Models Know When They Don't Know?
di: Zhou, Youchao, et al.
Pubblicazione: (2025)

KnowBias: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
di: Pan, Jinhao, et al.
Pubblicazione: (2026)

AgentGate: A Lightweight Structured Routing Engine for the Internet of Agents
di: Cheng, Yujun, et al.
Pubblicazione: (2026)